<h1 id="new-york-congressional-districts">2020 New York Congressional
Districts</h1>
<h2 id="redistricting-requirements">Redistricting requirements</h2>
<p><a
href="https://www.nysenate.gov/sites/default/files/ckeditor/Oct-21/ny_state_constitution_2021.pdf">In
New York, districts must</a>:</p>
<ol type="1">
<li>be contiguous (III.4(c)(3))</li>
<li>have equal populations (III.4(c)(2))</li>
<li>be geographically compact (III.4(c)(4))</li>
<li>preserve cores of existing districts, political subdivisions, and
communities of interest (III.4(c)(5))</li>
<li>not be drawn to discourage competition (III.4(c)(5))</li>
<li>not be drawn to favor or disfavor incumbents (III.4(c)(5))</li>
<li>not be drawn to favor or disfavor parties (III.4(c)(5))</li>
<li>not abridge minority group vote power (III.4(c)(1))</li>
</ol>
<h3 id="algorithmic-constraints">Algorithmic Constraints</h3>
<p>We enforce a maximum population deviation of 0.5%. We preserve cores
of the many geographic regions by using a pseudo county constraint.</p>
<h2 id="data-sources">Data Sources</h2>
<p>Data for New York comes from the ALARM Project’s <a
href="https://alarm-redist.github.io/posts/2021-08-10-census-2020/">2020
Redistricting Data Files</a>.</p>
<h2 id="pre-processing-notes">Pre-processing Notes</h2>
<p>Islands are connected to their nearest point on land.</p>
<h2 id="simulation-notes">Simulation Notes</h2>
<p>We sample 40,000 districting plans for New York across 2 independent
runs of the SMC algorithm. We then thin the sample to down to 5,000
plans. We apply a pseudo-county algorithmic constraint, which encourages
keeping together counties in less populated counties and municipalities
in the largest counties. The boundary here is set at the size of one
district, so Bronx County, Erie County, Kings County, Nassau County, New
York County, Queens County, Suffolk County, and Westchester County use
municipalities over counties. The core constraint here is unclear, as
the number of districts have changed, and because it is crossed with
preserving other communities. As such, the pseudo-county constraint
should weakly preserve the cores, as the prior map generally held
together counties and municipalities. A small population tempering value
was used to avoid losing diversity at the final step based on initial
runs.</p>
<h2 id="contents">Contents</h2>
<ul>
<li><code>NY_cd_2020_stats.csv</code> contains summary statistics on the
sampled redistricting plans</li>
<li><code>NY_cd_2020_plans.rds</code> is a compressed
<code>redist_plans</code> object, which contains the matrix of
precinct/block assignments and may be used for further analysis.</li>
<li><code>NY_cd_2020_map.rds</code> is a compressed
<code>redist_map</code> object, which contains the precinct/block
shapefile and demographic data.</li>
</ul>
<p>Both the <code>redist_plans</code> and <code>redist_map</code> object
are intended to be used with the <a
href="https://alarm-redist.github.io/redist/">redist package</a>.</p>
<h3 id="codebook-for-summary-statistics">Codebook for summary
statistics</h3>
<ul>
<li><code>draw</code>: unique identifier for each sample. Non-numeric
draw names are real-world plans, e.g., <code>cd_2010</code> for an
enacted 2010 plan.</li>
<li><code>district</code>: a district identifier. District numbers
roughly match those in the enacted plan, but the correspondence is not
perfect.</li>
<li><code>chain</code>: a number identifying the run of the
redistricting algorithm used to produce this draw. Used for diagnostic
purposes.</li>
<li><code>pop_overlap</code>: a number indicating the fraction of people
in this plan who reside in the same-numbered district in the enacted
plan.</li>
<li><code>total_pop</code>: the total population of each district.</li>
<li><code>total_vap</code>: the total voting-aged population of each
district.</li>
<li><code>pop_*</code>, <code>vap_*</code>: total (voting-aged)
population within racial and ethnic groups for each district. Variable
codes documented <a
href="https://github.com/alarm-redist/census-2020#data-format">here</a>.</li>
<li><code>plan_dev</code>: the maximum population deviation among
districts in the plan. Computed as
<code>max(abs(distr_pop - target_pop)/target_pop)</code>.</li>
<li><code>comp_edge</code>: compactness, as measured by the fraction of
internal edges kept. Higher values indicate more compactness.</li>
<li><code>comp_polsby</code>: compactness, as measured by the
Polsby-Popper score. Higher values indicate more compactness.</li>
<li><code>county_splits</code>: the number of counties which belong to
more than one district.</li>
<li><code>muni_splits</code>: the number of Census Designated Places
which belong to more than one district.</li>
<li><code>*_##_dem_*</code>, <code>*_##_rep_*</code>: vote counts for
statewide Democratic and Republican candidates in a certain election.
More information <a
href="https://github.com/alarm-redist/census-2020#data-format">here</a>.</li>
<li><code>adv_##</code>, <code>arv_##</code>: average vote counts for
statewide Democratic and Republican candidates in a certain year. More
information <a
href="https://github.com/alarm-redist/census-2020#data-format">here</a>.</li>
<li><code>ndv</code>, <code>nrv</code>: averages of the
<code>adv_##</code> and <code>arv_##</code> variables across all
available elections.</li>
<li><code>ndshare</code>: normal Democratic share, computed as
<code>ndv / (ndv + nrv)</code></li>
<li><code>e_dvs</code>: average Democratic vote share, computed as the
average of the Democratic vote share when first scored under each
statewide election.</li>
<li><code>pr_dem</code>: probability seat is represented by a Democrat;
calculated as the fraction of statewide elections under which the
district had a majority Democratic share.</li>
<li><code>e_dem</code>: expected number of Democratic seats for the
plan; equivalent to summing the <code>pr_dem</code> values across
districts</li>
<li><code>pbias</code>: partisan bias at 50% vote share, averaged across
all available elections. Positive values indicate Republican bias.</li>
<li><code>egap</code>: the efficiency gap, averaged across all available
elections. Positive values indicate Republican bias.</li>
</ul>
